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<main>
<article id="content">
<header>
<h1 class="title">Module <code>silk.matching.mnn</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python"># Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import cv2
import torch
import torch.nn.functional as F
from silk.logger import LOG


def compute_dist(desc_0, desc_1, dist_type=&#34;dot&#34;):
    assert dist_type in {&#34;dot&#34;, &#34;cosine&#34;, &#34;l2&#34;}

    if dist_type == &#34;dot&#34;:
        distance = 1 - torch.matmul(desc_0, desc_1.T)
    elif dist_type == &#34;cosine&#34;:
        desc_0 = F.normalize(
            desc_0,
            p=2,
            dim=1,
        )
        desc_1 = F.normalize(
            desc_1,
            p=2,
            dim=1,
        )
        distance = 1 - torch.matmul(desc_0, desc_1.T)
    elif dist_type == &#34;l2&#34;:
        distance = torch.cdist(desc_0, desc_1, p=2)

    return distance


def double_softmax_distance(desc_0, desc_1, temperature=1.0):
    similarity = torch.matmul(desc_0, desc_1.T) / temperature
    matching_probability = torch.softmax(similarity, dim=0) * torch.softmax(
        similarity, dim=1
    )
    return 1.0 - matching_probability


def match_descriptors(
    distances,
    max_distance=torch.inf,
    cross_check=True,
    max_ratio=1.0,
):
    indices1 = torch.arange(distances.shape[0], device=distances.device)
    indices2 = torch.argmin(distances, dim=1)

    if cross_check:
        matches1 = torch.argmin(distances, dim=0)
        mask = indices1 == matches1[indices2]
        indices1 = indices1[mask]
        indices2 = indices2[mask]

    if max_distance &lt; torch.inf:
        mask = distances[indices1, indices2] &lt; max_distance
        indices1 = indices1[mask]
        indices2 = indices2[mask]

    if max_ratio &lt; 1.0:
        best_distances = distances[indices1, indices2]
        distances[indices1, indices2] = torch.inf
        second_best_indices2 = torch.argmin(distances[indices1], axis=1)
        second_best_distances = distances[indices1, second_best_indices2]
        second_best_distances[second_best_distances == 0] = torch.finfo(
            torch.double
        ).eps
        ratio = best_distances / second_best_distances
        mask = ratio &lt; max_ratio
        indices1 = indices1[mask]
        indices2 = indices2[mask]

    matches = torch.vstack((indices1, indices2))

    return matches.T


def swap_xy(given_ordering, required_ordering, positions):
    assert given_ordering in {&#34;yx&#34;, &#34;xy&#34;}
    assert required_ordering in {&#34;yx&#34;, &#34;xy&#34;}

    if given_ordering == required_ordering:
        return positions

    return positions[..., [1, 0]]


def mutual_nearest_neighbor(
    desc_0,
    desc_1,
    distance_fn=compute_dist,
    match_fn=match_descriptors,
):
    dist = distance_fn(desc_0, desc_1)
    matches = match_fn(dist)
    return matches


def ransac(matched_points_0, matched_points_1, ordering=&#34;xy&#34;):
    assert len(matched_points_0) == len(matched_points_1)

    if len(matched_points_0) &lt; 4:
        LOG.warning(
            f&#34;ransac cannot be run, only {len(matched_points_0)} were provided (&lt;4)&#34;
        )
        return None

    matched_points_0 = swap_xy(ordering, &#34;xy&#34;, matched_points_0)
    matched_points_1 = swap_xy(ordering, &#34;xy&#34;, matched_points_1)

    matched_points_0 = matched_points_0.detach().cpu().numpy()
    matched_points_1 = matched_points_1.detach().cpu().numpy()

    estimated_homography, _ = cv2.findHomography(
        matched_points_0,
        matched_points_1,
        cv2.RANSAC,
    )

    if estimated_homography is not None:
        estimated_homography = torch.tensor(
            estimated_homography,
            dtype=torch.float32,
        )

    return estimated_homography


def batched_ransac(matched_points_0, matched_points_1, ordering=&#34;xy&#34;):
    return [
        ransac(mp0, mp1, ordering)
        for mp0, mp1 in zip(matched_points_0, matched_points_1)
    ]


def estimate_homography(
    points_0,
    points_1,
    desc_0,
    desc_1,
    matcher_fn=mutual_nearest_neighbor,
    homography_solver_fn=ransac,
    ordering=&#34;xy&#34;,
):
    assert ordering in {&#34;xy&#34;, &#34;yx&#34;}

    matches = matcher_fn(desc_0, desc_1)
    matched_points_0 = points_0[matches[:, 0]]
    matched_points_1 = points_1[matches[:, 1]]

    estimated_homography = homography_solver_fn(
        matched_points_0[:, :2],
        matched_points_1[:, :2],
        ordering,
    )

    return (
        estimated_homography,
        matched_points_0,
        matched_points_1,
    )


def batched_estimate_homography(
    points_0,
    points_1,
    desc_0,
    desc_1,
    matcher_fn=mutual_nearest_neighbor,
    homography_solver_fn=ransac,
    ordering=&#34;xy&#34;,
):
    estimated_homography = []
    matched_points_0 = []
    matched_points_1 = []

    for args in zip(
        points_0,
        points_1,
        desc_0,
        desc_1,
    ):
        result = estimate_homography(
            *args,
            matcher_fn=matcher_fn,
            homography_solver_fn=homography_solver_fn,
            ordering=ordering,
        )
        estimated_homography.append(result[0])
        matched_points_0.append(result[1])
        matched_points_1.append(result[2])

    return estimated_homography, matched_points_0, matched_points_1</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="silk.matching.mnn.batched_estimate_homography"><code class="name flex">
<span>def <span class="ident">batched_estimate_homography</span></span>(<span>points_0, points_1, desc_0, desc_1, matcher_fn=&lt;function mutual_nearest_neighbor&gt;, homography_solver_fn=&lt;function ransac&gt;, ordering='xy')</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def batched_estimate_homography(
    points_0,
    points_1,
    desc_0,
    desc_1,
    matcher_fn=mutual_nearest_neighbor,
    homography_solver_fn=ransac,
    ordering=&#34;xy&#34;,
):
    estimated_homography = []
    matched_points_0 = []
    matched_points_1 = []

    for args in zip(
        points_0,
        points_1,
        desc_0,
        desc_1,
    ):
        result = estimate_homography(
            *args,
            matcher_fn=matcher_fn,
            homography_solver_fn=homography_solver_fn,
            ordering=ordering,
        )
        estimated_homography.append(result[0])
        matched_points_0.append(result[1])
        matched_points_1.append(result[2])

    return estimated_homography, matched_points_0, matched_points_1</code></pre>
</details>
</dd>
<dt id="silk.matching.mnn.batched_ransac"><code class="name flex">
<span>def <span class="ident">batched_ransac</span></span>(<span>matched_points_0, matched_points_1, ordering='xy')</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def batched_ransac(matched_points_0, matched_points_1, ordering=&#34;xy&#34;):
    return [
        ransac(mp0, mp1, ordering)
        for mp0, mp1 in zip(matched_points_0, matched_points_1)
    ]</code></pre>
</details>
</dd>
<dt id="silk.matching.mnn.compute_dist"><code class="name flex">
<span>def <span class="ident">compute_dist</span></span>(<span>desc_0, desc_1, dist_type='dot')</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def compute_dist(desc_0, desc_1, dist_type=&#34;dot&#34;):
    assert dist_type in {&#34;dot&#34;, &#34;cosine&#34;, &#34;l2&#34;}

    if dist_type == &#34;dot&#34;:
        distance = 1 - torch.matmul(desc_0, desc_1.T)
    elif dist_type == &#34;cosine&#34;:
        desc_0 = F.normalize(
            desc_0,
            p=2,
            dim=1,
        )
        desc_1 = F.normalize(
            desc_1,
            p=2,
            dim=1,
        )
        distance = 1 - torch.matmul(desc_0, desc_1.T)
    elif dist_type == &#34;l2&#34;:
        distance = torch.cdist(desc_0, desc_1, p=2)

    return distance</code></pre>
</details>
</dd>
<dt id="silk.matching.mnn.double_softmax_distance"><code class="name flex">
<span>def <span class="ident">double_softmax_distance</span></span>(<span>desc_0, desc_1, temperature=1.0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def double_softmax_distance(desc_0, desc_1, temperature=1.0):
    similarity = torch.matmul(desc_0, desc_1.T) / temperature
    matching_probability = torch.softmax(similarity, dim=0) * torch.softmax(
        similarity, dim=1
    )
    return 1.0 - matching_probability</code></pre>
</details>
</dd>
<dt id="silk.matching.mnn.estimate_homography"><code class="name flex">
<span>def <span class="ident">estimate_homography</span></span>(<span>points_0, points_1, desc_0, desc_1, matcher_fn=&lt;function mutual_nearest_neighbor&gt;, homography_solver_fn=&lt;function ransac&gt;, ordering='xy')</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def estimate_homography(
    points_0,
    points_1,
    desc_0,
    desc_1,
    matcher_fn=mutual_nearest_neighbor,
    homography_solver_fn=ransac,
    ordering=&#34;xy&#34;,
):
    assert ordering in {&#34;xy&#34;, &#34;yx&#34;}

    matches = matcher_fn(desc_0, desc_1)
    matched_points_0 = points_0[matches[:, 0]]
    matched_points_1 = points_1[matches[:, 1]]

    estimated_homography = homography_solver_fn(
        matched_points_0[:, :2],
        matched_points_1[:, :2],
        ordering,
    )

    return (
        estimated_homography,
        matched_points_0,
        matched_points_1,
    )</code></pre>
</details>
</dd>
<dt id="silk.matching.mnn.match_descriptors"><code class="name flex">
<span>def <span class="ident">match_descriptors</span></span>(<span>distances, max_distance=inf, cross_check=True, max_ratio=1.0)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def match_descriptors(
    distances,
    max_distance=torch.inf,
    cross_check=True,
    max_ratio=1.0,
):
    indices1 = torch.arange(distances.shape[0], device=distances.device)
    indices2 = torch.argmin(distances, dim=1)

    if cross_check:
        matches1 = torch.argmin(distances, dim=0)
        mask = indices1 == matches1[indices2]
        indices1 = indices1[mask]
        indices2 = indices2[mask]

    if max_distance &lt; torch.inf:
        mask = distances[indices1, indices2] &lt; max_distance
        indices1 = indices1[mask]
        indices2 = indices2[mask]

    if max_ratio &lt; 1.0:
        best_distances = distances[indices1, indices2]
        distances[indices1, indices2] = torch.inf
        second_best_indices2 = torch.argmin(distances[indices1], axis=1)
        second_best_distances = distances[indices1, second_best_indices2]
        second_best_distances[second_best_distances == 0] = torch.finfo(
            torch.double
        ).eps
        ratio = best_distances / second_best_distances
        mask = ratio &lt; max_ratio
        indices1 = indices1[mask]
        indices2 = indices2[mask]

    matches = torch.vstack((indices1, indices2))

    return matches.T</code></pre>
</details>
</dd>
<dt id="silk.matching.mnn.mutual_nearest_neighbor"><code class="name flex">
<span>def <span class="ident">mutual_nearest_neighbor</span></span>(<span>desc_0, desc_1, distance_fn=&lt;function compute_dist&gt;, match_fn=&lt;function match_descriptors&gt;)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def mutual_nearest_neighbor(
    desc_0,
    desc_1,
    distance_fn=compute_dist,
    match_fn=match_descriptors,
):
    dist = distance_fn(desc_0, desc_1)
    matches = match_fn(dist)
    return matches</code></pre>
</details>
</dd>
<dt id="silk.matching.mnn.ransac"><code class="name flex">
<span>def <span class="ident">ransac</span></span>(<span>matched_points_0, matched_points_1, ordering='xy')</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def ransac(matched_points_0, matched_points_1, ordering=&#34;xy&#34;):
    assert len(matched_points_0) == len(matched_points_1)

    if len(matched_points_0) &lt; 4:
        LOG.warning(
            f&#34;ransac cannot be run, only {len(matched_points_0)} were provided (&lt;4)&#34;
        )
        return None

    matched_points_0 = swap_xy(ordering, &#34;xy&#34;, matched_points_0)
    matched_points_1 = swap_xy(ordering, &#34;xy&#34;, matched_points_1)

    matched_points_0 = matched_points_0.detach().cpu().numpy()
    matched_points_1 = matched_points_1.detach().cpu().numpy()

    estimated_homography, _ = cv2.findHomography(
        matched_points_0,
        matched_points_1,
        cv2.RANSAC,
    )

    if estimated_homography is not None:
        estimated_homography = torch.tensor(
            estimated_homography,
            dtype=torch.float32,
        )

    return estimated_homography</code></pre>
</details>
</dd>
<dt id="silk.matching.mnn.swap_xy"><code class="name flex">
<span>def <span class="ident">swap_xy</span></span>(<span>given_ordering, required_ordering, positions)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def swap_xy(given_ordering, required_ordering, positions):
    assert given_ordering in {&#34;yx&#34;, &#34;xy&#34;}
    assert required_ordering in {&#34;yx&#34;, &#34;xy&#34;}

    if given_ordering == required_ordering:
        return positions

    return positions[..., [1, 0]]</code></pre>
</details>
</dd>
</dl>
</section>
<section>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="silk.matching" href="index.html">silk.matching</a></code></li>
</ul>
</li>
<li><h3><a href="#header-functions">Functions</a></h3>
<ul class="">
<li><code><a title="silk.matching.mnn.batched_estimate_homography" href="#silk.matching.mnn.batched_estimate_homography">batched_estimate_homography</a></code></li>
<li><code><a title="silk.matching.mnn.batched_ransac" href="#silk.matching.mnn.batched_ransac">batched_ransac</a></code></li>
<li><code><a title="silk.matching.mnn.compute_dist" href="#silk.matching.mnn.compute_dist">compute_dist</a></code></li>
<li><code><a title="silk.matching.mnn.double_softmax_distance" href="#silk.matching.mnn.double_softmax_distance">double_softmax_distance</a></code></li>
<li><code><a title="silk.matching.mnn.estimate_homography" href="#silk.matching.mnn.estimate_homography">estimate_homography</a></code></li>
<li><code><a title="silk.matching.mnn.match_descriptors" href="#silk.matching.mnn.match_descriptors">match_descriptors</a></code></li>
<li><code><a title="silk.matching.mnn.mutual_nearest_neighbor" href="#silk.matching.mnn.mutual_nearest_neighbor">mutual_nearest_neighbor</a></code></li>
<li><code><a title="silk.matching.mnn.ransac" href="#silk.matching.mnn.ransac">ransac</a></code></li>
<li><code><a title="silk.matching.mnn.swap_xy" href="#silk.matching.mnn.swap_xy">swap_xy</a></code></li>
</ul>
</li>
</ul>
</nav>
</main>
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